Land Use Change - Science topic

Good day Prem, thank you for writing. Wish you find some good food for thoughts in it.

James, thank you for commenting. The reason I wrote that article in 2002 was that I saw moved towards a single focus on human causes only isolated from even the idea that earth is more than humans.

All the aspects you highlighted Jams are consistent with the implication of the simple formula I shared in the article and the piece by piece puzzle implications.

Dear researcher "Frontiers in Remote Sensing" typically refers to the cutting-edge advancements and research areas within remote sensing technology and applications. It encompasses innovative methodologies, new sensor technologies, and novel applications of remote sensing data across various disciplines such as environmental monitoring, urban planning, agriculture, and disaster management. Academic discussions in this field often focus on pushing the boundaries of spatial and spectral resolution, enhancing data fusion techniques, improving accuracy through machine learning and AI, and exploring emerging trends like hyperspectral and LiDAR remote sensing. This dynamic field plays a crucial role in understanding our planet's dynamics and supporting informed decision-making globally

Jean-Pierre Wigneron

I am intrested

You might find this article useful

In the UK, one of the supermarkets was trying not to stock products containing palm oil because of the deforestation, but had to reinstate it in 2022 because of their supply chain following the war in the Ukraine:

https://sustainability.iceland.co.uk/our-planet/sustainable-sourcing/palm-oil/

UPDATE on Frontiers in Earth Observation and Land Monitoring!

An important change has occurred: prior to its launch, Frontiers in Earth Observation and Land Monitoring has been merged with Frontiers in Remote Sensing, and I'm the new Field Chief Editor of the journal, with the goal of making it a leading journal in the field of remote sensing.

Notably, four new thematic sections will be added to Frontiers in Remote Sensing (Terrestrial Water Cycle, Terrestrial Carbon Cycle, Land Use Change, and Extreme Events/Climate), making the journal much more visible for applications of remote sensing.

On the occasion of this merging, we have some waivers for free publications

Please contact me at jean-pierre.wigneron@inrae.fr

Thank you!

Iceland volcano erupts on Reykjanes peninsula (BBC, 4 hours ago). Volcanic eruptions, always Fascinating in Beauty and Majesty, remind us in a spectacular way of essential factors in the heat balance of the globe: the transfers at the Visible Lithosphere-Atmosphere Interface in the form of Seismic and Volcanic Activities and the transfers at the Lithosphere-Hydrosphere interface, Invisible because they occur at the bottom of the oceans. Unlike the GHE, the effects of these activities on Climate Change are not well analyzed, at least in Climate Models, including those used in IPCC projections.

Illustration Source: ICELANDIC MET OFFICE:

https://www.researchgate.net/post/Climate_Change_and_Climate_Models_Progress_and_Limits

Great job, you can validate in two ways:

1. use Google earth or

2. Ground truthing (actual fieldwork visit with selected georeferenced points)

Hello

Mr Bhatta

Could you please send me the guidelines?

E-mail: dlara.ylmaz94@gmail.com

Thanks..

"Assessing the Climate and Landuse Changes on Land Subsidence in Plains, Alluvial Fans, and Metropolitan Areas" encompasses several crucial components. Firstly, understanding the climate patterns is imperative as it directly influences land subsidence. By analyzing historical climate data and predicting future trends, researchers can identify how temperature, precipitation, and extreme weather events impact subsidence rates. Additionally, assessing land use changes is vital to comprehend their correlation with subsidence. This involves studying urbanization patterns, agricultural practices, and deforestation rates to determine their contribution to land sinking phenomena. Moreover, investigating the geological characteristics of plains, alluvial fans, and metropolitan areas is essential. Factors such as soil composition, groundwater levels, and geological faults play a significant role in land subsidence. By conducting thorough geological surveys and utilizing advanced imaging techniques like satellite remote sensing or LiDAR technology, researchers can gain valuable insights into these elements. Furthermore, incorporating hydrological studies is crucial for comprehending the impact of water extraction on land subsidence. Understanding the intricate relationship between groundwater pumping, surface water availability, and land subsidence is vital for sustainable water management in these areas. Additionally, monitoring and regulating the rate of water extraction can help mitigate the adverse effects of land subsidence. Implementing effective measures such as groundwater recharge projects, where excess surface water is directed back into aquifers, can help replenish depleted groundwater levels and alleviate subsidence issues. Moreover, promoting alternative water sources like desalination or recycled wastewater can reduce reliance on groundwater extraction and minimize the risk of further land subsidence. Collaborative efforts between scientists, policymakers, and local communities are necessary to develop comprehensive strategies that address the complex interplay between land subsidence and water management. By adopting a holistic approach that integrates geological surveys, hydrological studies, and sustainable water practices, we can safeguard our landscapes from the detrimental impacts of land subsidence while ensuring a secure water future for generations to come.

You can give SAR coherence for snow cover mapping a try.

Dear Hamza Ali Minhas,

You have chosen an interesting and future-oriented topic for your research. As part of your preferred subject matter, I propose the following research topic: Determinants of the increase in the importance of biological regeneration of civilization degraded areas in the context of the progressing process of global warming and the negative effects of this process. As part of this topic, one can also study the growing importance of the development of sustainable organic farming as an important factor of agricultural land use change and a significant factor in reducing greenhouse gas emissions, counteracting the progressing global warming process, producing food for people with a reduced scale of environmental pollution, producing agricultural crops without contributing to the reduction of the population of bees and other pollinating insects, solving the problem of hunger in many countries with low levels of economic development. As part of this topic, the use of new information technologies, ICT and Industry 4.0, can also be considered in order to improve the techniques and monitoring of biological regeneration processes carried out in civilization degraded areas.

Best regards,

Dariusz Prokopowicz

Kory Postma thank you very much for your response.

Yaseen A Al-Janabi What I meant by agricultural land use was somewhat different, but thanks for your comment anyway and I agree with your point about the introduction of contemporary irrigation systems. Because land and water management practices specifically start in the field not outside of it.

I made a mistake in my answer. I gave you the information for the COntingency coefficient. For the Cramer V, there is a table of effect size which depend on the degree of freedom of the Cramer V. See: https://www.real-statistics.com/chi-square-and-f-distributions/effect-size-chi-square/

Hope you get some idea from the following paper

Dear Majid,

You may use Landsat satellites to cover these years. You may also increase the spatial resolution to 15 meters using pan sharping technique. About the classes, many methods can be applied whether supervised or non. It depends in the analysis and purposes. Envi and Erdas are good choices to apply that.

Best wishes,

Jasem

Yes indeed.

I am interested!

That is a good question.

band 4, 5, 6 is the best three-band combination and band 1, 2, 5, 7 is the best four-band combination which achieved almost identical performance as using all bands for LULC classification.

Thanks for the question. The answers also interest me

Dear Pritiranjan Das

Here are some of the most prominent models for simulating urban growth. Besides, Jiří Kroc has provided a very good approach as well.

Thanks and regards

Gowhar Meraj

You must see what are the upper and lower issues in the planning system and how they have linked and intertwisted and find the gaps. Sometimes these gaps are regulative and technical. However, in the global south countries, we do not have strong regulative support and always third parties involve to take over the resources in the name of any kind of development. Furthermore, you might need to look at transnational policies and guidelines and how the national and sub-national decision-makers work with them. This is what in geography we call place-space and how specific resource (might be kind of green development) is favorable for the national and international market.

Hi Jake,

It is a daunting task and not very easy to answer. We have been struggling with it for a while now. From our experiences it comes back to two basic challenges. The first one is how you define land degradation (see our recent publication below), but of course also to obtain the proper datasets. In our research we are focusing on creating a time series of a range of soil properties. We are currently aiming to at a time series of soil conditions for the UN statistics division in the context of their national accounting.

Cheers Jetse

Sterk, G., Stoorvogel, J.J., 2020. Desertification–Scientific Versus Political Realities. Land 2020, 9, 156; doi:10.3390/land9060156.

Stoorvogel, J.J., Bakkenes, M., ten Brink, B.J.E., and Temme, A.J.A.M., 2017. To what extent did we change our soils? A global comparison of natural and current conditions.. Land Degrad. Develop., doi: 10.1002/ldr.2721.

Stoorvogel, J. J., Bakkenes, M., Temme, A.J.A.M., Batjes, N.H., and ten Brink, B.J.E., 2017. S-World: A Global Soil Map for Environmental Modelling. Land Degrad. Develop., 28: 22–33. doi: 10.1002/ldr.2656.

@Pir Mohammad

I will try. Thank you so much for your valuable comments and suggestions.

Most GIS and RS software have the standard classification methods (ArcGIS, QGIS, ENVI, Eedas etc).

Considerable land changes have been recorded periodically world over. Dynamics and spatial determinants of land change could be achieved through integration of decadal (10 year gap) cover maps. Landsat images covering spatiotemporal socioeconomic database need be reinforced with collective evidence (ground truth). They are covered under Land Use and Land Cover Change (LULCC)

Land change is a result of cropland (fallow) conversion and forest area changes excluding commercial plantations. Crop land to fallow conversions occur due to many reasons – lack of irrigation facilities, capital investment, labour shortage or even fragmentation of land holdings.

Comparative figures for Human land use (world-55%; India-83%); Climate change (world-20-24%; India 25-30%) and Land degradation (world-8-41%; India -57%) are some examples. However, the land classification includes many more classes as well.

Is there a specific reagion you need to generate a land use change chart?

hi Sudheer Bhakare

So, first you need to match/convert your Bhuvan's AWiFS LULC data classes into 24 classes of USGS classification. You can find the classes in the WRF user guide in the Chapter 3.

After the translation, you will have your data in the correct classification to insert in WRF. Now you need to convert the TIFF file (I am expecting you LULC data is a raster file) into binary format (BIL) required by WRF.

I usually use the convert_geotiff function from openWRF, which you ca find at https://github.com/openwfm/convert_geotiff

This tool will convert the tiff into binary and also will create the index file. This index file holds the information of your data, like resolution, description, extension, etc. (take a look in the other LULC in the geog folder).

After the conversion in binary format, move this files to a folder inside the geog data location (take care to not override the WRF default data)

The next step is to edit the GEOGRID.TBL file inside the WPS folder.

The GEOGRID.TBL holds the information of all land surface data to WPS/WRF, so you just need to edit the Land use section of the file, add one "name" to your data, set the interpolation method, and set the path to your data/folder.

This will helps WPS to find your data and how to proceed with it.

Next step is to edit your namelist.wps.

When you look at it, you will see the geog path is set to default.

for example, a wrf configuration of two domains, you will see something like this:

geog_data_res = 'default','default'

you just need to edit this line into

geog_data_res = 'name_you_set_in_TBL + default', ' name_you_set_in_TBL + default'

So, WPS will look first at the "name_you_set_in_TBL" data, and extract the LULC data to your domain. After extract your data, WPS will look at the default data to extract the other parameter to WRF

The chapter 3 of the user guide also helps you to set this variables, edit the GEOGRID.TBL, and namelis.wps.

Regards

conversion of digital number to reflectance can be done in ArcGIS.

The steps involved in this process:

Geodata ----> Data types ----> Rasters and Images -----> Processing and analyzing raster data ------> Apparent reflectance function

The process is applicable to sensors like Landsat, IKONOS, and QuickBird.

From a southern perspective, I will go for option 2. Like @Luis Fernando indicated, we need to rethink our understanding of city making especially in the global south. Obviously the city is being shaped largely by the people as opposed to 'our plans' - let's plan from the peoples perspective (inclusive, participatory planning). Miraftab's piece on 'insurgent planning' will offer you a clearer insight of my point of departure. Hope this helps

40 seconds ago

Try arcMap it have many indicators to separate land us, you need someone have expereince in this programme..

Regards@

Hi Sarowar,

A good strategy is to run a parallel unsupervised classification and check out the spectral signatures of your training samples. This can inform you about the spectral differences/similarities between your expert classes. You can use this information to split your 'barren lands' and/or 'buildup' into multiple classes. Having more classes might improve the strength of the classification and you can always regroup the classes afterwards.

Regards,

Maarten

The modified vegetation indices (such as EVI and MSAVI) are generally aimed to reduce the effects of atmosheric attenuation and soil background. The form of these complex formula can be determined or inspired by radiative transfer theory and also soil line theory. The coefficients are determined empirically or based on training datasets.

Trial and error method can be used to develop new indices, but the new indices should in most cases be supported by physical principles. Even for the machine learning method, those important independent variables (related to the dependent variable) are generally selected for learning.

Hello,

Geoprospective is a difficult discipline to manipulate, not because it deals with the future, but because humans are like the Nature they are from : unpredictable. So, there is a possible solution : using together the nomothetic (spatial analysis) which explains us what is permanent through time (and so, we can suppose it will still be the case in 50 or 100 years from now) but also axiology which deals with ethics, esthetic but also (that's a personnal opinion) with cognitive science. Axiology is not permanent, even if scientific methods were tried on it, ethics change through time and between societies, esthetic too and i don't talk even talk of minds (the cultural matter) ! ... The mixing of those two sides should produce something interesting, as long as there is a perfect logical, reasoning and objective work.

See you here or there.

Térence

Dear when you perform classification, it is already give you the full thematic map and add to that thematic map for each class

regards

Interesting insights on the matter may be provided by the ESPON SUPER project (Sustainable Urbanisation and Land Use in European Regions).

The inception report is published already, and the interim report should follow in a month or so.

Hope this helps!

Hi Phan Cao,

I think that ENVI (Environment for Visualizing Imagery) has a low entry level which allows you to get to image classification fast. The algorithms are state-of-the art for many applications including land use and land cover classification in supervised and unsupervised classification modes.

Hence have a look at the follwinf site to know more about this software, which I have been using for years now in remote sensing applications. It can also include Interactive Diagramming Language (IDL) when you need to program very specific appliations.

Here is the website of ENVI:

Lots of success,

Frank

Hello! I refer to DINAMICA EGO, which is developed here at the research center where I work. Any help with DINAMICA, I am available!

Dear

THis is fairly common problem in LULC mapping. It is always possible to validate a recent map, since the land cover/land use can be checked for accuracy through field visit. However, for past LULC maps, all you can do is to use the maximum trusted data source you might have at your disposal which can be checked against your past remotely sensed maps. It can either be aerial photographs, reference LULC maps, ancillary data.

A common approach people often use is sampling from Google Earth satellite images. They are free but you cannot download them, but rather just browse them. However, since Google is providing a timeline feature for free, aiming at your area of interest, you can freely access old picture and sample data for each class of interest. Lots of published papers use this approach for validation when ancillary sources/aerial photographs or reference datasets for the past years are not available, yet it has proven to be a realiable method (see http://adsabs.harvard.edu/abs/2015SPIE.9644E..1NR and ). I can direct you to a tutorial here (https://ibis.geog.ubc.ca/courses/geob373/labs/IGETT_Exercises/google_earth_for_ground_truthing.html).

Also find attached to this answer the FAO Map Accuracy Assessment and Area Estimation practical guide advocating the same method for sampling data from Google Earth for map validation, along with formulas and a link to an interesting plugin developed to this end, CollectEarth (http://www.openforis.org/tools/collect-earth.html).

Hope this helps.

if you search in the literature you will find many examples of lumped models based on stepwise multiple regression that include rainfall , landuse, evapotranspiration etc. I published a paper (sometime ago) Rainfall-Runoff Relationship and Water Budget for Zarqa River Basin I will add the full text to my profile ... hope that help

Hi Naveed Ahmed. I normally analyze LU change scenarios as follows. First I calibrate and validate SWAT with the current LU scenario or whatever your baseline scenario is. Next I will re-run the model but modifying the LU input raster according with the scenarios of interest, and using the calibrated parameters that you came up with during the Calibration process. This approach implies that you create your scenarios of interest out of the model according with the problem that you want to analyze. One simple example would be analyzing a deforestation risk map you may identify future scenarios. SWAT also allows you to analyze BMP inside the model.

I also agreed with Gustavo Alckmin

Have a look this group discussion

https://groups.google.com/d/msgid/swat-cup/dce44cc4-4800-4618-b5fb-47c5a68be7ae%40googlegroups.com.

Hello!

Make sure all the input files present in the same project directory. If not place them into same folder.

valuable discussion

Grazie Claudia per l'invito che terrò presente.

Paolo

Hi Aydin,

I recommend this tutorial to quantify Landcover's changes in space and time and the use of markov chains: http://benbestphd.com/landscape-ecology-labs/lab2.html

Also, for this purpose you can use the lulcc package : https://spiral.imperial.ac.uk/bitstream/10044/1/41199/3/gmd-8-3215-2015.pdf

And this paper:

y can write matlab code or use mathwork.com

Hi Roland,

Do you mean that the SWAT_CUP copys the lup.dat but It does not copy the associated files (HRUs fraction files)?

If this is the case, might be you could try to copy the land use change fraction files to the working folder of SWAT_CUP for it.

About the .hru changing constantly but the HRU fraction does not change. I think this is because every time the parameters was change, all of the file will be rewrite by SWAT_CUP. SWAT_CUP does not need to change the HRU_FR in .hru file because SWAT will change it according to the information in the lup.dat file.

I also create an R script to generate input files (TxtInOut) for SWAT (new HRUs emerings due to land use change will also be consider and you can also interpolated you HRU fraction at any time). If you interest in using this script, please let me know.

Yes, they are the grid locations based on the [row range, column range] of the NetCDF file instead of their 'latitude and longitude' or x, y position as shown in GrADS. Thanks, the problem is solved.

Thanks.

Dear Jasasvini Peranantham

Please have a look at these useful RG links.

Good luck!

Thank you for your answer Dr Salem,

Kind regards

Hi Massimo and Kenneth,

Thank you for answering/commenting my question. The 58.275Ha are inclusive of roads, buildings, parking lots, shopping malls and similar infrastructure. From my score here above I would like to figure out the actual number of Ha. that are cultivated within urban areas. Thanks!

Roland,

I see no reason why Chi-squared test cannot be used. The data you wish to collect is nominal data. The problem is the sampling procedure that would suit best for extracting your data. Do you apply systematic or random sampling. That depends largely on the nature of the spatial patterns present on the maps. Probably a method of random sampling would be preferable.

Certainly relate land use/types specific to similar time sets. Once data has been gathered and tabulated for running the Chi test, set up your null hypothesis, I.e. that there is NO relationship between . . . While composite tests can be done, I think testing pair by pair is better. Composite testing can obscure the main relationship.

Having said that, I am not sure but that other graphic and/or statistical methods may prove more useful, e.g. trend graphs or histograms. They require more interpretation than Chi values but can give much more useful information, especially concerning changes over time.

George

You can also trip MODIS or ASTER products.

Check the links below;

Wishes

Dear you need to convert DN to radiance then convert the radiance to reflectance by using Band math tool

See my answer to your personal message. Also look at the excel example there and on the previous answer above. Note you have to have the SOLVER ADD-IN activated in EXCEL. The idea is to have an objective (like minimize costs) and constraints.

Je suis plus phytosociologue mais je vous recommande l ouvrage de Hachid M. ''Aux sources de l Afrique 50 siècles avant les pyramides

Interested

Dear Tine, as the roots are mostly water - when you dry the sample (to measure the total dry weight) you will have removed most of the mass associated with the roots anyway. When you have collected the samples (ie from a fixed volume) and you are preparing them for drying, you could try to remove the larger roots without taking any attached soil - this may not be easy or possible if the soil has a lot of clay in it. The error of including fine roots is likely to be less than that of the actual sampling method (particularly the errors associated with compaction when driving cores into the ground or the trimming of samples once removed). I would like to follow up on a comment from Gerben. It is worth calculating the density of the fine earth fraction (ie the density of the soil between stones as this gives you an idea of the relative density that roots experience (ie related to mechanical impedance) if you are comparing sites with and with out stones - of layers with different stone content. This may not strictly be the "bulk" density of the overall soil, but it is important information depending on your reason for measuring it is. If you are measuring bulk density to convert carbon content of mass/mass basis to mass/area basis, then you will need to know the volume of fine fraction (whole sample minus stone volume) to be able to calculate the mass/area.

There are several LUCC models for free. One quite widespread and famous is Dinamica Ego (http://csr.ufmg.br/dinamica/). You also have the CLUE model (http://www.ivm.vu.nl/en/Organisation/departments/spatial-analysis-decision-support/Clue/index.aspx) developed by a research team in Netherlands.

Also in this paper ( An open and extensible framework for spatially explicit land use change modelling: The lulcc R package) you can find the presentation of a R package which includes two LUCC models, being CLUE one of them if I am remember well.

For a general overview, you have short presentations of several LUCC models in the last part of this book: http://www.springer.com/us/book/9783319608006

I have given you above the links to some of them. However, I am sure there are another ones different which are also for free.

Thanks very much for the feedback!

Best regards,

Currently we are working on dehesa in Andalusia (see attached). Further, for your consideration a somewhat older article from dehesa in Extremadura and the most recent historical overview I have come across.

Evidently, we are interest in your experience with P. cinnamomi in dehesa or otherwise in oak, if any.

Hi there. The following is my opinion, references mostly from memory, caveat emptor!

1) agent-based models;

hard to calibrate, especially if there are many agents, not always spatially explicit. Probably better for understanding the process of change rather than creating future simulations, especially if the main goal is to understand the interaction of the agents of change. But recent work is beginning to address these limitations, e.g. see the CRAFTY model (Murray-Rust et al)

2) artificial neural networks;

rules of transition are calculated by the neural network and are invisible to the user - the model is effectively a "black box", these models have a tendency to overfit to the calibration date. On the other hand, they do produce good simulations by standard goodness-of-fit measures. See Pijanowski and collaborators, from 2005 (Grand Traverse Bay Watershed Michigan and numerous other cases up to the present day)

3) cellular automata;

most practical examples rely on trial and error calibration, rather than empirical evidence of association between land use and drivers. But transition rules are transparent (unlike ANN) since the user must explicitly define them. The modeller achieves a fuller understanding of the change processes and they perform well on pattern-based or cell-by-cell statistical measures. Convincingly model the dynamics of urban growth. See work by Keith Clarke and collaborators (1997, 1998 etc), White and Engelen 1993, White et al 1997 (city of Cincinnati). See also work on automatic or semi-automatic calibration of these models - Straatman, B., White, R., & Engelen, G. (2004). Towards an automatic calibration procedure for constrained cellular automata. Computers, Environment and Urban Systems, 28(1), 149-170, and also more recent work (see esp. Barreira-Gonzalez et al 2015, and Norte Pinto and Pais Antunes 2010)

4) economics-based models; and

a range of econometric models exist that claim to reliably associate the size of cities with three key drivers, distance from central business district, agricultural land value, and population. Problem is that this strong association at small spatial scales (e.g. all US cities) breaks down at large spatial scales (e.g within an individual region). Also does not really explain the process or pattern of change. See Brueckner and Fansler 1984, etc etc also critique by Bockstael and Irwin 1999

We talked about some of these issues in a recent working paper, maybe its of interest.

5) Markov chains;

Don't know, I've not looked into these in detail.

Hope this helps!